Large language models (LLMs) have historically relied on dense architectures where every weight participates in every inference.
The Mixture-of-Experts (MoE) paradigm offers a different path: it divides a model into many specialized “expert” sub-networks and activates only a subset of them for each input.
In practical terms, an MoE model can vastly increase its total parameter count without a proportional increase in computation per token.
This approach is highly relevant to efficient LLM scaling – it enables sparse computation so that model capacity can grow while keeping training and inference costs in check. Recent breakthroughs like DeepSeek’s models demonstrate the power of MoE in scaling up LLMs.
DeepSeek-V3, for example, packs 671 billion parameters in total but activates only 37 billion per token by “picking” the most relevant experts instead of using the entire network every time.
This DeepSeek MoE architecture delivers both high capacity and efficiency, making it possible to achieve GPT-4-level performance at a fraction of the usual computational expense.
In this article, we dive into the MoE architecture used in DeepSeek-R1 and DeepSeek-V3 models, examining how it works, its technical details, performance benchmarks, and what it means for the future of efficient LLM scaling.
Section 1: Overview of DeepSeek-R1 and V3 MoE Models
DeepSeek-V3 was unveiled in late 2024 as a massive MoE language model that set a new milestone for open-source AI.
With 671B parameters (37B active per token), DeepSeek-V3 demonstrated that MoE could scale model size dramatically without an equivalent jump in compute requirements. Instead of a single colossal neural network, V3 is composed of many expert networks. During inference, only a fraction of these experts are invoked for a given token, which is why only ~37 billion parameters’ worth of weights are active at any time.
This design makes the model extremely powerful yet computationally efficient. Notably, DeepSeek-V3 achieved this scale at a training cost of only $5.6 million (about 2.79 million GPU hours on NVIDIA H800s), whereas a comparable dense model like GPT-4 likely cost an order of magnitude more (estimates range $50–100M).
The model was released under an open-source license (MIT), with both a base version and a Chat-tuned version available for the community.
The base model was pre-trained on massive web text and ebooks, while the chat model received further instruction tuning and RLHF to excel in conversational tasks (reportedly comparing favorably to GPT-4 and other frontier models).
DeepSeek-V3 comes with a 128K context window, far exceeding the context length of earlier open models and even many commercial ones, which enhances its ability to handle long documents and multi-step reasoning.
Just weeks after V3’s debut, DeepSeek-R1 was introduced (early 2025) as a refined reasoning-focused model built atop the V3 base.
While R1 retains the same MoE architecture and size (671B parameters, 37B active) its training regimen was geared toward complex problem-solving and “thinking” capabilities.
DeepSeek-R1 underwent a novel multi-stage training pipeline: initially a purely reinforcement learning (RL) approach without supervised fine-tuning (a phase released as R1-Zero) and later a fine-tuned phase with carefully curated data plus RL to polish its reasoning skills.
The result is a model that doesn’t just output answers but also generates a step-by-step chain-of-thought explaining its reasoning. This makes R1 particularly powerful for tasks like advanced mathematics, coding challenges, scientific reasoning, and multi-step planning.
Impressively, R1’s additional training was very cost-effective – roughly $294K in compute beyond the base model, bringing the total training budget to around $6M.
Despite this modest cost, DeepSeek R1’s model matches or even surpasses proprietary counterparts in key benchmarks: for instance, R1 achieved 91.6% on the MATH reasoning benchmark and excels in code generation, rivaling OpenAI’s own reasoning model (code-named “o1”) on math, coding, and logic tests.
Like V3, R1 supports a 128,000-token context and is fully open-source, making cutting-edge reasoning AI accessible to developers without the hefty API fees.
In summary, DeepSeek-V3 and DeepSeek-R1 showcase how a well-crafted MoE architecture can produce frontier-level LLM performance (comparable to GPT-4 class models) in an open, cost-efficient manner.
Section 2: Technical Breakdown of DeepSeek’s MoE Setup
DeepSeek’s architecture follows the standard Transformer backbone with a twist: all the Feed-Forward Network (FFN) layers from DeepSeek-V2 onward use a Mixture-of-Experts design. In practice, this means each Transformer layer (or most of them) contains not one monolithic FFN sub-layer but an MoE layer composed of many parallel expert FFNs.
The routing mechanism is handled by a learned gating network which decides, for every input token at every MoE layer, which subset of experts will be used.
DeepSeek’s gating network computes a score for each expert based on the token’s hidden state; then it performs Top-K gating, selecting the highest-scoring experts and ignoring the rest.
According to technical reports, DeepSeek-V3’s MoE layers consist of an extremely high number of experts – on the order of 256 experts per layer (plus a few shared experts) – and the gate selects the top 8 experts per token to activate.
In other words, each token’s forward pass through an MoE layer is handled by only 8 out of 256 specialized sub-networks, rather than one giant network. Those 8 expert outputs are combined (usually via a weighted sum of their outputs, using the gate’s softmax-normalized scores as weights) to produce the layer’s output.
The gating decision is made independently for each token, not at the sequence level – a crucial detail that allows fine-grained routing even within a single sequence.
Number of experts and structure:
In DeepSeek-V3, each MoE layer has a mix of “shared” experts and “routed” experts. A few shared experts (reported as 3 per layer) are always active for every token – these are meant to capture common, generalizable knowledge that every token can benefit from.
The remaining experts (256 per layer) are routed experts that specialize in different aspects of language or knowledge domains; any given token will activate only a small subset of these.
DeepSeek’s research found that traditional MoE models with a limited number of large experts tended to suffer from knowledge hybridity (each expert ends up handling very diverse information) and knowledge redundancy (different experts learn the same common features). By finely segmenting into more numerous, smaller experts and reserving some as shared repositories of general knowledge, DeepSeek’s MoE (sometimes referred to as DeepSeekMoE) achieves greater expert specialization and reduces parameter duplication.
For example, earlier versions like DeepSeekMoE-16B used many small experts and showed comparable performance to a dense LLaMA-2 7B model despite using only ~40% as many active parameters per token. In the full DeepSeek-V3, this design scales up dramatically – the DeepSeek-R1 and V3 models each have 671B total parameters spread across hundreds of experts, yet only ~5% of those weights are active at a time (37B of 671B).
In effect, the architecture behaves like a committee of specialists: each token “consults” a handful of expert networks, making the forward pass much sparser (and faster) than a dense network of equal size.
Routing and load balancing:
The routing is implemented via a single linear projection (the gating network) that outputs a score for each expert, followed by selection of top-$K$ experts. A softmax on the top-$K$ scores gives the gating weights $g_{i,t}$ for the chosen experts, which are then used to weight the experts’ outputs.
One challenge with MoE is ensuring the workload is balanced across experts – without precautions, some experts could get most of the traffic while others are rarely used, hurting training efficiency.
DeepSeek addressed this with an auxiliary-loss-free load balancing strategy (an improvement over earlier MoE approaches that used auxiliary balancing losses).
Although the detailed solution is complex, the key idea is to avoid the performance penalty of forcing equal load via extra loss terms. Instead, DeepSeek’s adaptive routing encourages a natural spread of tokens across experts (techniques likely include random routing noise or capacity limits per expert, as hinted by literature).
The result is that all experts are trained and utilized sufficiently, preventing any single expert from becoming a bottleneck or collapsing (a common issue in naive MoE implementations).
This careful routing mechanism is crucial to DeepSeek’s success – it ensures that each token finds the most relevant experts quickly, and that the vast capacity of 256 experts/layer is effectively used.
The outcome is an MoE architecture where the effective model size (capacity) is huge, but the computation per token remains more akin to a 30B–40B parameter model, striking an excellent balance between scalability and efficiency.
Section 3: Benchmark Comparisons – DeepSeek vs GPT-3.5/GPT-4, LLaMA 2, Mistral
DeepSeek’s MoE models have made waves by delivering GPT-4 class performance at substantially lower cost, and benchmark results back this up. In terms of raw capabilities, DeepSeek-R1’s performance is on par with the best models from OpenAI and others in complex reasoning tasks.
The R1 model scores in the top percentile on challenging evaluations: for example, it achieved 91.6% on the MATH dataset, a result that is in the vicinity of GPT-4 and significantly ahead of GPT-3.5. On coding benchmarks, R1 similarly shines – one report noted it ranks in the 89th percentile on Codeforces coding challenges, which is comparable to top-tier proprietary models.
DeepSeek themselves have indicated that R1 rivals or surpasses OpenAI’s o1 model (a GPT-4-grade reasoning LLM) in mathematics, coding, and logical reasoning tasks.
This is remarkable considering R1 is open-source and was trained for under $6 million, whereas GPT-4’s training likely cost tens of millions of dollars.
When comparing DeepSeek V3 performance to other open models, the advantages of MoE become clear. DeepSeek-V3’s chat-tuned model has been benchmarked against models like LLaMA 2 and newer open releases.
On general NLP benchmarks (MMLU, BIG-bench, etc.), DeepSeek-V3 (671B sparse) outperforms dense models of far smaller size – even LLaMA2’s 70B model cannot match V3’s broad knowledge and generation quality given the sheer gap in effective parameters.
In specialized domains, the gap is even wider: for instance, DeepSeek-V3’s March 2025 update (V3-0324) leveraged some of R1’s reinforcement learning tricks and outperformed GPT-4.5 in coding and math evaluations.
It’s important to note that these DeepSeek models only use about 37B parameters per inference, yet they behave as if they were much larger (because different tokens tap into different portions of the 671B pool of knowledge).
This is efficiency in action – a dense model would have to compute every one of those 671B weights for each token, incurring enormous cost, whereas DeepSeek’s sparse activation avoids that overhead.
Looking at cost-performance tradeoffs, DeepSeek is a clear win. OpenAI’s GPT-4 API (and presumably the upcoming GPT-4.5) is not only closed-source but also expensive to use (e.g., roughly $0.03–0.06 per 1K tokens).
By contrast, DeepSeek-R1 can be self-hosted and runs at a fraction of that cost – one analysis pegged it at about $0.55 per million input tokens on cloud GPU instances, compared to around $15 per million for OpenAI’s model.
This ~27× cost advantage is transformative for teams with limited budgets. Moreover, R1’s open model license means developers can fine-tune it, inspect its outputs (including its transparent reasoning traces), and deploy it without restrictions, which is not possible with proprietary models.
In the open-source arena, other projects are also exploring MoE for better performance. The Mistral model (by Mistral AI) is often cited for its efficiency at smaller scale – their 7B dense model topped some leaderboards in mid-2023.
Mistral reportedly experimented with a MoE variant dubbed “Mixtral,” featuring 8 experts per layer and ~46.7B total parameters, with only ~12.9B active per token. This design (activating 2 of 8 experts, i.e. top-2 gating) is conceptually similar to DeepSeek’s (though on a much smaller scale).
It illustrates the same principle: the model behaves like a ~13B model at inference time but has the knowledge capacity of nearly 47B, which improved its performance beyond standard 13B models.
DeepSeek simply took this sparse scaling idea to the extreme – with 256 experts per layer, top-8 activation, and an unprecedented total parameter count.
For context, DeepSeek’s full 671B model uses roughly 18× more parameters per token than LLaMA 2 70B (37B vs 70B) yet contains almost 10× the total parameters in its reservoir, which explains its superior results in knowledge-intensive tasks.
Another comparison: an early DeepSeekMoE-16B model was shown to match LLaMA2-7B performance while using only 2.8B active parameters (LLaMA2-7B uses all 7B per token) – a testament to MoE’s efficiency.
Finally, it’s worth noting that DeepSeek’s models also excel in context length and reasoning transparency, which are harder to quantify but important. With a 128K context, R1 and V3 can handle inputs far beyond GPT-3.5 or GPT-4’s typical 4K-32K window.
This is especially useful for processing long documents or multi-turn conversations without losing information. R1’s chain-of-thought feature (where it internally and optionally externally generates reasoning steps) is another differentiator; while OpenAI’s models have an internal reasoning process, they don’t explicitly share it.
R1, by design, can output its “thinking” in <think>...</think> tags, giving users insight into how it solved a problem. This transparency is valuable for debugging and trust in high-stakes applications. Models like GPT-4, LLaMA 2, or Mistral do not provide that out-of-the-box.
In summary, DeepSeek R1 and V3 outperform or match similarly-sized dense models on most metrics, and even compete with proprietary giants like GPT-4, all while dramatically lowering the cost-per-performance ratio.
The combination of open-source availability, massive scale via MoE, and targeted optimization for reasoning makes DeepSeek’s offerings especially appealing to researchers and developers aiming for cutting-edge performance without breaking the bank.
Section 4: Code Example – MoE Routing and Expert Activation (PyTorch Pseudocode)
To better understand how a DeepSeek-like MoE layer functions, let’s walk through a simplified PyTorch-style pseudocode for an MoE feed-forward layer.
In a real DeepSeek implementation, this logic is highly optimized and distributed across many GPUs, but the following snippet captures the high-level idea of routing and expert activation in an MoE architecture:
import torch
import torch.nn.functional as F
class MoELayer(torch.nn.Module):
def __init__(self, hidden_size, expert_intermediate, num_experts, top_k=8):
super().__init__()
# Gating network: produces scores for each expert
self.gate = torch.nn.Linear(hidden_size, num_experts)
# Define experts as individual feed-forward networks
self.experts = torch.nn.ModuleList([
torch.nn.Sequential(
torch.nn.Linear(hidden_size, expert_intermediate), # W1
torch.nn.GELU(), # activation (could be SwiGLU or others)
torch.nn.Linear(expert_intermediate, hidden_size) # W2
)
for _ in range(num_experts)
])
self.top_k = top_k
def forward(self, x):
# x shape: [batch_size, seq_len, hidden_size]
batch_size, seq_len, hidden = x.shape
# Flatten the sequence and batch to process tokens individually
x_flat = x.view(-1, hidden) # shape: (batch_size * seq_len, hidden_size)
# Compute expert scores for each token
scores = self.gate(x_flat) # shape: (batch_size * seq_len, num_experts)
# Select top-k experts for each token
top_vals, top_idx = torch.topk(scores, self.top_k, dim=-1) # indices of selected experts
# Convert scores to probabilities (gating weights) for the chosen experts
top_probs = F.softmax(top_vals, dim=-1) # shape: (batch_size*seq_len, top_k)
# Prepare output tensor
output_tokens = torch.zeros_like(x_flat) # (batch_size*seq_len, hidden_size)
# Route each token's representation through its selected experts
for token_idx in range(x_flat.size(0)):
token_input = x_flat[token_idx] # one token's hidden state
experts_for_token = top_idx[token_idx] # expert indices selected for this token
expert_weights = top_probs[token_idx] # corresponding weights
# Sum contributions from each chosen expert
combined_output = torch.zeros(hidden, device=x.device)
for j, expert_id in enumerate(experts_for_token):
# Apply expert network to the token
expert_out = self.experts[expert_id](token_input)
# Weight the expert's output by the gating weight
combined_output += expert_weights[j] * expert_out
output_tokens[token_idx] = combined_output
# Reshape back to [batch_size, seq_len, hidden_size]
return output_tokens.view(batch_size, seq_len, hidden)
In this pseudocode, each token in the input decides its top-$K$ experts via the gate network.
Only those experts are applied to the token, and their outputs are combined by learned weights. For example, if top_k=2, a token might route to Expert #5 and #17 with weights 0.7 and 0.3, respectively, meaning the final output is 0.7 * Expert5(token) + 0.3 * Expert17(token). This illustrates the core of a Mixture-of-Experts LLM: most of the experts are skipped for a given token, saving computation. In DeepSeek’s actual code, the routing would be vectorized and parallelized – grouping tokens by expert, processing each expert’s batch in parallel, and so on – to handle large batches efficiently.
Libraries like DeepSpeed and FasterTransformer provide optimized kernels for such operations, enabling the model to scale across many GPUs.
A few implementation details to note:
- Expert Networks: Here we modeled each expert as a simple two-layer feed-forward (with a GELU activation). DeepSeek uses a SwiGLU activation and may have an additional linear (as part of the gated linear unit), but the idea is similar. All experts within a layer share the same architecture and size, but their weights are different, allowing them to specialize. In DeepSeek-V3, the hidden size is ~7168 and each expert’s intermediate size is 2048 (smaller than the dense counterpart’s intermediate size).
- Gating Network: We used a linear layer to produce expert scores from the token’s hidden state. DeepSeek’s gate is essentially the same – a learned projection that outputs a scalar score for each of the 256 experts. The gating network is trained alongside the experts, so it learns to predict which experts will best improve the model’s loss for a given token. A sigmoid or softmax is typically applied during training to get probabilities; DeepSeek uses a variant of softmax over the top-$K$ to get the $g_i,t$ weights.
- Top-K Routing: We show an example of brute-force looping for clarity. In practice, one would use efficient scatter/gather operations. The
torch.topkfunction gives the indices of the top experts for each token. Frameworks will then dispatch each token to its experts. For example, one can create mini-batches of tokens for Expert 5, Expert 17, etc., and process each expert’s batch in one forward call (this avoids looping in Python). DeepSeek’s high-performance implementation ensures minimal overhead in this routing process, which is critical given thousands of tokens and hundreds of experts. - Combining Outputs: The weighted sum of expert outputs is the final output of the MoE layer for that token. DeepSeek additionally adds the outputs of the shared experts to this sum (these are like always-active experts with effectively a fixed weight of 1). Thus, $h’t = \sum{\text{shared } i} \text{FFN}{s_i}(u_t) + \sum{j \in \text{Top-8}} g_{j,t} \cdot \text{FFN}_{r_j}(u_t)$. The presence of shared experts means every token gets some common background processing, while the routed experts contribute task-specific processing.
This code example, while simplified, demonstrates how routing and expert activation work in an MoE layer.
The key takeaway for developers is that an MoE layer can be integrated into a Transformer model quite naturally – substituting a standard FFN with a gated collection of FFNs.
Modern deep learning frameworks have begun to support this pattern, making it increasingly feasible to experiment with Mixture-of-Experts LLM architectures for efficient scaling.
Section 5: Scalability Insights – Training Efficiency, Inference Speed, and Hardware Impact
The MoE architecture in DeepSeek brings significant scalability benefits, fundamentally altering the economics of training and deploying large models.
Here we discuss how DeepSeek’s design impacts training cost, inference speed, and hardware utilization:
Training Cost Efficiency:
By activating only a small fraction of the model’s parameters at each step, DeepSeek drastically reduces the floating-point operations (FLOPs) required per token compared to a dense model of equal size. This translates directly into lower training time and cost.
DeepSeek-V3’s training run (with 671B total params) cost only ~$5.6M in GPU compute, roughly one-tenth the cost of training a model like GPT-4 (which is rumored to use a similarly large or larger number of parameters).
The MoE approach allowed DeepSeek to utilize a conditional compute strategy – effectively training many sub-models (experts) simultaneously on different subsets of data, coordinated by the gating mechanism.
This means more parameters learned per GPU-hour. The scaling law at play is that as long as the dataset is large enough, adding parameters (experts) improves performance, but thanks to sparse activation, the cost grows sub-linearly.
DeepSeek’s own papers noted that at ~2B dense-equivalent size, their MoE was already reaching the theoretical upper-bound of performance for that compute budget. In essence, MoE allowed them to push model capacity to the limit of what the data could support, without being bottlenecked by exponential cost.
Another aspect is precision and optimization – DeepSeek used tricks like FP8 low-precision training to further cut down compute and memory usage, and techniques like Multi-Head Latent Attention (MLA) to reduce memory for attention KV cache.
These engineering optimizations, combined with MoE, yielded an unprecedented 10× improvement in training efficiency over previous state-of-the-art models.
Inference Speed and Throughput:
At inference time, the sparse activation means DeepSeek-R1 and V3 only compute ~37B parameters’ worth of operations for each token, making them much faster than a dense 671B model would be.
In fact, the models behave like a ~30-40B parameter model in terms of latency, while leveraging a much larger knowledge base.
This is a game-changer for deployment: tasks that might have been prohibitively slow on a 600B+ dense model can run with reasonable latency on DeepSeek.
Moreover, MoE enables parallel processing across experts – since different experts can run simultaneously on different hardware.
DeepSeek’s MoE experts are distributed across a cluster of GPUs, which means that when a token is routed to (say) Expert 5, the computation happens on the GPU holding Expert 5’s weights, in parallel with other tokens being processed by other experts on other GPUs. This parallelism can improve throughput significantly.
One analysis found that adding more GPUs for DeepSeek inference actually increases utilization efficiency – as you scale out, each GPU’s throughput goes up because the workload is better balanced and synchronized across experts.
In practice, DeepSeek can use techniques like dynamic batching and cache reuse to further speed up generation. The vLLM library, for example, has been used with DeepSeek models to achieve high token throughput by smartly managing the distributed attention cache and expert calls.
It’s worth noting that while the gating computation is relatively small (just a matrix-vector product per token), it can introduce some overhead and complexity.
If not well-optimized, the routing step could become a bottleneck (especially if too many tokens choose the same expert, causing a queue). DeepSeek mitigated this with their balanced routing strategy, ensuring no single expert stalls the pipeline.
The bottom line is that inference with DeepSeek’s models is surprisingly practical given their size – developers report running DeepSeek-R1 on multi-GPU servers with speed on par with dense models that have a fraction of the parameters.
Memory and Hardware Impact:
Hosting a 671B-parameter model is no small feat, but MoE makes it more feasible by partitioning the model across hardware. In a dense model, all layers’ weights must be present (or swapped) on each device that processes the model.
In DeepSeek’s MoE, experts can be sharded across GPUs. For example, with 256 experts per layer, one could distribute, say, 32 experts to each of 8 GPUs (just as an illustration).
During inference, each token will only need to communicate with the 8 GPUs that host its selected experts (plus the few shared experts which could be replicated or also sharded).
This means the memory load per GPU is just a slice of the full 671B parameters. DeepSeek’s architecture likely employed this kind of sharded deployment to handle the model – essentially using model parallelism at the expert level.
The context length of 128K does impose additional memory usage for attention caches, but DeepSeek’s MLA (Multi-Head Latent Attention) reduces the per-token memory overhead of attention by compressing keys and values.
The combination of FP8 precision and memory optimizations allowed DeepSeek to run inference with relatively affordable hardware (reports mentioned using NVIDIA H800, which is an 80GB memory GPU, and needing a cluster of them, but nothing exotic like TPU pods exclusively).
In terms of FLOPs, an MoE model spares a huge amount of multiplication-add operations by not activating all weights. DeepSeek-V3’s 37B active parameters would roughly correspond to on the order of 74 billion FLOPs per token (for multiply-adds, if each weight is used once).
If the entire 671B were active, it would be an order of magnitude more FLOPs per token, which would drastically slow down generation.
Thus, MoE not only cuts theoretical compute, it also cuts energy usage and hardware wear proportionally. This is one reason DeepSeek’s achievement has been called a paradigm shift – it showed that clever architecture can overcome brute-force hardware advantages.
In fact, the cost-effectiveness of DeepSeek’s MoE models has been disruptive in the industry, prompting discussions that scaling laws may favor sparse models for the next generation of LLMs.
In summary, DeepSeek’s MoE design yields a model that is cheaper to train, faster to infer, and easier to distribute than a dense-model counterpart of similar capability.
By dramatically cutting the active FLOPs and spreading the model across hardware, DeepSeek made a 671B model usable in practice.
The trade-offs include added complexity in the model implementation and training dynamics (routing is tricky to get right, and network communication becomes a factor), but DeepSeek’s success suggests these were well-managed.
For researchers and engineers, this is a powerful lesson: efficient LLM scaling via MoE can unlock performance that would be unattainable with dense models on a given budget or piece of hardware.
Conclusion
The DeepSeek MoE architecture represents a significant leap in how we think about scaling AI models.
By leveraging Mixture-of-Experts at an unprecedented scale, DeepSeek’s team delivered models that rival the best from tech giants while remaining open and budget-friendly. This has enormous implications for the AI community.
For one, it democratizes access to high-performance LLMs – an academic lab or a small company can now experiment with a 671B-parameter model (or its distilled offspring) without needing access to exclusive supercomputers. It also validates MoE as a viable strategy for the future of model scaling.
Scaling dense models to trillions of parameters is extraordinarily costly and may hit diminishing returns, whereas scaling via more experts allows capacity to grow cheaply as long as data and expert specialization keep pace.
We may see more projects adopting MoE or hybrid sparse approaches (e.g., mixtures of experts, retrieval-augmented models, etc.) to push the envelope of model performance.
DeepSeek’s design choices – such as fine-grained experts, shared experts for common knowledge, and RL-driven reasoning tuning – have set a template that others can follow or improve upon.
The fact that DeepSeek-R1 was peer-reviewed and published (unusual for a state-of-the-art LLM) also brings a measure of transparency to this space. It encourages a culture of open, rigorous evaluation for large models, which can only benefit the field.
From an engineering perspective, the success of DeepSeek’s MoE models has spurred developments in software frameworks (for better MoE support) and even hardware (we might imagine future GPUs/TPUs optimizing sparse compute patterns).
For resource-constrained researchers, the appeal of DeepSeek’s approach is clear: you can train a model with tens of times more parameters without tens of times more compute.
There is of course no free lunch – MoE models are complex and require very careful training to ensure those parameters are actually useful – but DeepSeek has shown it’s possible to do at scale.
Already, we see new MoE-based LLMs emerging (as hinted by projects like Mistral Mixtral, Qwen-3 MoE, etc.), drawing inspiration from DeepSeek’s blueprint.
In conclusion, DeepSeek’s MoE architecture is a milestone in efficient LLM design. It delivers massive scale through sparse activation, achieving a sweet spot of performance vs. cost that was previously out of reach.
This has leveled the playing field between open-source initiatives and the well-funded closed models in terms of raw capability.
Going forward, it wouldn’t be surprising if Mixture-of-Experts (and related efficient architectures) become standard practice for cutting-edge AI – a development that could lead to even larger, more intelligent models that are still usable by the wider community.
DeepSeek has cracked open the door to trillion-parameter models by showing that efficient LLM scaling is not only possible but practical, and that is an exciting prospect for the future of AI research and development.